What is pandas append function?

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If you work with data in Python, you’ve likely encountered the pandas library. It’s one of the most powerful tools for data manipulation and analysis. Among its many functions, the append() function in pandas is commonly used when combining data from different sources. In this comprehensive guide, we’ll answer the question: What is pandas append function? We’ll explore how it works, its use cases, limitations, and alternatives, along with plenty of examples.

Introduction to pandas

Before diving into the append() function, let’s briefly cover what pandas is. Pandas is an open-source Python library built for data manipulation and analysis. It provides data structures like Series and DataFrame that allow for efficient handling of structured data.

What is pandas append function?

The append() function in pandas is used to add rows of one DataFrame to the end of another DataFrame. It’s a quick and simple way to concatenate two or more dataframes vertically.

Syntax:

DataFrame.append(other, ignore_index=False, verify_integrity=False, sort=False)

Parameters:

  • other: The DataFrame or Series to append.
  • ignore_index: If True, the resulting DataFrame will not retain the original index values.
  • verify_integrity: If True, it checks for duplicate indices and raises an error if any are found.
  • sort: If True, sorts columns if the columns of the two DataFrames are not aligned.

Returns:

A new DataFrame containing the combined data.

Note: As of pandas 1.4.0, the append() method is deprecated and will be removed in a future version. It’s recommended to use pandas.concat() instead for appending dataframes.

Basic Example

import pandas as pd

# Create two sample DataFrames
df1 = pd.DataFrame({
    'Name': ['Alice', 'Bob'],
    'Age': [25, 30]
})

df2 = pd.DataFrame({
    'Name': ['Charlie', 'David'],
    'Age': [35, 40]
})

# Append df2 to df1
result = df1.append(df2)
print(result)

Output:

      Name  Age
0    Alice   25
1      Bob   30
0  Charlie   35
1    David   40

In this example, df2 is appended to df1, and the resulting DataFrame includes all rows from both.

Using ignore_index

To reset the index after appending, you can set ignore_index=True:

result = df1.append(df2, ignore_index=True)
print(result)

Output:

      Name  Age
0    Alice   25
1      Bob   30
2  Charlie   35
3    David   40

Appending a Single Row

You can also append a single row (as a dictionary or Series):

new_row = {'Name': 'Eve', 'Age': 28}
result = df1.append(new_row, ignore_index=True)
print(result)

Output:

    Name  Age
0  Alice   25
1    Bob   30
2    Eve   28

Appending with Mismatched Columns

If the DataFrames have mismatched columns, pandas will fill missing columns with NaN:

df3 = pd.DataFrame({
    'Name': ['Frank'],
    'Age': [45],
    'City': ['New York']
})

result = df1.append(df3, ignore_index=True)
print(result)

Output:

    Name   Age      City
0  Alice  25.0       NaN
1    Bob  30.0       NaN
2  Frank  45.0  New York

When Not to Use append()

While append() is convenient, it may not be the best choice in all situations:

  • Performance: Using append() repeatedly inside a loop is inefficient because it creates a new DataFrame each time.
  • Deprecation: As mentioned, append() is deprecated in newer pandas versions.
  • Better Alternatives: pd.concat() is more flexible and efficient for combining DataFrames.

Alternative: Using pandas.concat()

result = pd.concat([df1, df2], ignore_index=True)
print(result)

This is the recommended way to append DataFrames, especially when working with large datasets.

Use Case: Combining Monthly Sales Data

Imagine you receive sales data each month in separate CSV files. Here’s how you might use append() (or preferably concat()) to combine them:

import pandas as pd

# Read each CSV file into a DataFrame
jan = pd.read_csv('sales_jan.csv')
feb = pd.read_csv('sales_feb.csv')
mar = pd.read_csv('sales_mar.csv')

# Combine them all
all_data = pd.concat([jan, feb, mar], ignore_index=True)

# Do analysis on the combined data
print(all_data.describe())

Similar Functions for Combining DataFrames

Besides append() and concat(), pandas offers several other functions for combining, merging, and joining data. Each has its own use case:

  • merge(): Similar to SQL joins; combines DataFrames based on common columns or indexes. Ideal for combining datasets with shared keys.
  • join(): A convenience method for joining columns of another DataFrame based on the index. Useful for aligning data on a shared index.
  • combine_first(): Updates missing values in one DataFrame with non-missing values from another. Great for filling in incomplete datasets.
  • update(): Updates values in a DataFrame with values from another, based on matching labels.

Each of these functions can be powerful when used in the right context, and understanding the differences will help you write more efficient, readable data transformation pipelines.

Summary: Key Takeaways

  • The append() function in pandas is used to add rows from one DataFrame to another.
  • It returns a new DataFrame; the original is not modified unless reassigned.
  • ignore_index=True resets the index in the result.
  • append() is deprecated; use pandas.concat() instead for better performance and future compatibility.
  • Avoid using append() in loops—gather data in a list and use pd.concat() once at the end.

Final Thoughts

So, what is pandas append function? It’s a once-popular way to combine rows from multiple DataFrames, especially for quick tasks or scripts. However, as pandas evolves, developers are encouraged to use concat() for more robust and scalable data handling.

Understanding the nuances of how dataframes can be merged or appended is essential for any data analyst or data scientist working with pandas. By mastering these tools, you’ll be better equipped to handle complex data workflows efficiently.

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